Best Ways AI Is Improving Manufacturing In 2024

Artificial Intelligence technology has the power to automate and revolutionizing the entire manufacturing ecosystem. From automating routine tasks, prototype designs and development, robot deployment and maintenance, and supply chain automation to facility remote monitoring, everything can be automated seamlessly by implementing AI in manufacturing.

To be competitive with other manufacturing companies, organizations need to accelerate with AI transformation and speed up smart manufacturing operations. While it may sound intimidating, Artificial Intelligence in manufacturing is also exciting, and it doesn’t go away.

The below figure depicts the market growth of AI technology in manufacturing industry for a period of 2015 to 2025.

Source: Markets and Markets

Top AI Applications In Manufacturing Industry
How AI transforms manufacturing?

Let’s continue reading to know where AI is being applied in the manufacturing sector and how AI works in manufacturing. All the below AI use cases in manufacturing will ensure the best business outcomes to the companies. Let’s take a look at How AI technology redefining the manufacturing industry?

1.AI Virtual Assistants Bots

It is one of the best use cases of AI in manufacturing industry. AI virtual assistants better communicates with workforce, solve queries, perform assigned tasks, and offer personalized recommendations, etc. Using Natural Language Processing capabilities, AI manufacturing applications deliver human-like responses and executes every task faster, efficient with 100% accuracy.

2. Predictive Maintenance and Analytics

The best answer is predictive maintenance. Yes. Adoption of AI technology in manufacturing industry will assist manufacturers in monitor and analyze the device performance and predict the need of overhaul or maintenance services. Such automated process will prevent the equipment breakdowns and ensure efficient productivity. It is one of the top benefits of AI in manufacturing industry.

Using Artificial intelligence in manufacturing industry, Manufacturers can tracks operational efficacy and derives accurate predictions into maintenance.

Hence, Artificial Intelligence solutions processes device data, learns from experiences, identifies patterns, and finds out the possibilities of damages before the product hauls. This kind of AI applications in manufacturing industry will improve productivity and ensures lower maintenance costs.

3.Process Automation 

Many routine tasks in a manufacturing plant will be automated with the trending AI. Intelligent AI applications in manufacturing industry will automate approximately 75% of routine tasks done by admins, managers, and operational department workforce.

From data entry, reports generation, employee monitoring, sales entries, order management, warehouse management, and inventory management to logistics and distribution operations data can be automated and virtually accessed through a centralized cloud platform. Hence, the importance of AI technology in manufacturing industry is paramount.

4. Facial Recognition Apps

Facial recognition is one of the core technology of AI. The use of AI in manufacturing industry for ensuring high-level security standards inside the production premises is on demand. In the manufacturing industry, AI applications with facial recognition capabilities can be used for quality and security purposes.

5. Fraud Detection

AI in manufacturing industry will be used for detecting fraudulent actions in the production, warehouse, and point of sales operations. Manufacturers can monitor identical anomalies to flag customer orders outside of normal patterns. The use of AI technology in manufacturing industry will also greatly helpful for organizations in monitoring the facilities and find irregular activities remotely.

These are the most popular and well-defined applications of AI in manufacturing. The future of artificial intelligence in manufacturing industries has bright scope and lets manufacturers grab the digital opportunities.

Also Recommended to Read: Use Cases of AI in Manufacturing

The Present State of AI Technology

Many industry scholars agree that AI has a big impact on all businesses, especially the manufacturing sector. IT service management company, Gartner said that by 2020, the number of users of advanced analytics platforms would be differentiated by increased data discovery capabilities, which will double the rate and double the business value”.

AI is embedded in several technologies, from networked supply chains using predictive analytics to ERP solutions with built-in-BI capabilities.

In a thought to give you a clear idea, here we are taking a reputed manufacturing company as an example, which has introduced AI to redefine manufacturing analytics.

Sight Machine Launched AI to Redefine Manufacturing Analytics

Sight Machine, a manufacturing Analytics Company, located in San Francisco, aims to disrupt the manufacturing industry by offering an Artificial intelligence-based manufacturing analytics platform that collects, improves and inspects information to acquire actionable insights and real-time visibility.

The company’s Factory TX platform provides manufacturers with the adaptability to obtain the data of machine from the cloud and production facilities. Their tool allows for quick deployment and centralized management of multiple industrial facility IoT data intake. Insights are generated in real-time and integrate device data, process quality data, and product data with installed Enterprise Resource Planning data and handled to produce important insights using Machine Learning and AI.

Benefits of Using AI in Sight Machine

The main advantage of AI in manufacturing is that it makes digital twin resources as well as procedures. Using AI, the plant management will track man, machine, material, and also process that drives ceaseless process development.

In addition, this platform helps in combining data from several manufacturers, historians, Enterprise Resource Planning, and other machines. Moreover, this platform helps improve quality, reduce time, increase the utilization of capacity, reduce scrap, measures and improves OEE and productivity, etc.
Artificial Intelligence will also play an important role in helping humans do their jobs. AI is just a tool, but not a replacement, for improving business insight and decision-making.

Impact Of AI In Manufacturing Industry

The term AI enables workers to visualize the future, where machines run themselves and robots are gradually welded and bolted to parts without human attention. As a large number of smart manufacturing companies grows, humans are still playing an important role in operations, and this is likely to continue for decades.

These false assumptions can lead to improvements and advanced applications. Employees may assume that AI technology coverts to reductions in the count of human jobs. Engineers, data scientists, and other technicians may mistakenly assume that the profession in manufacturing is short-lived and will soon be replaced by technology. Such fears could prevent the next generation of skilled professionals from even considering employment in the manufacturing industry.

Recommended to Read: The Revolution of AI Technology

The lack of skilled professionals in manufacturing is one of the main concerns of industry; About 426,000 Manufacturing jobs in the USA are not filled because there are not enough skilled applicants to obtain those. Yet, 80% of companies lack the workforce needed to implement AI projects.

Therefore, a mystery is created. As skilled workers are in a dilemma that AI technology will eliminate jobs, they might not applied for vacant positions. But the lack of skilled employees makes it tough to implement those sophisticated technologies.

There is a high demand for employees who are skilled in AI technology, but hiring candidates is not so simple.  Hence, A few manufacturers are giving training to them to develop internal AI expertise. One of the practical ways to gain experience is turning to software providers and third-party resources.

Final Words

Leveraging new and trending technologies and updating processes like AI technology and Machine Learning are keys to remaining competitive and relevant in the manufacturing industry. Artificial Intelligence is one of the most effective and powerful platform that manufacturers can adopt. As Manufacturing firms need to focus on the future with utmost confidence, they will make active decisions.

If you are also planning to take advantage of Artificial Intelligence for your manufacturing company, contact us.
Our USM AI Professionals will provide you complete information.

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Machine Learning in Healthcare: The Future Of Healthcare Will Be Around ML 

ML In Healthcare: Application, Benefits, and App Development Cost

Machine Learning (ML) is one of the trending concepts in the field of Artificial Intelligence (AI). Driven by its automation and predictive analytics, ML technology is being used for creating intelligent software solutions that offer more accurate analysis of data.

Banking and finance for Fraud detention, Marketing & sales business for predicting market scope and user interests, Healthcare organizations for improving patient care services, and Fintech organizations for estimating stock trends, ML is widely used across diversified industries.

Be it the purpose of forecasting the market dynamics, analyzing & optimizing equipment performance, determining customer behaviors, or making deep analyses of sales data, ML-powered and AI-based mobile apps have the highest scope in the years ahead. The industries will increasingly gain a lot of operational and financial benefits from using ML’s automation and predictive potentialities.

Among all other industries, the healthcare industry is one of the top sectors that is an early adopter of AI and ML-like advanced technological innovations. Whether you are a startup or a fully developed brand in the healthcare industry, ML app development ensures streamlined business operations. Let’s start our session with the top applications of ML in the healthcare sector.

Today, in this article, we would like to discuss the top use cases of ML in healthcare, the significant benefits of ML in the healthcare industry, and how much will it cost to develop ML-based healthcare apps.

Top Use Cases Of AI/ML In Healthcare Industry

The significance of machine learning in healthcare is extending with the continuous developments in the field of AI. Speed and accuracy as the core features, and ML technology is making a buzz in the digital world. Let’s take a look at the significant applications of ML in the Healthcare industry.

Here are the best answers for How AI is used in healthcare.

1. Disease Prediction

Disease prediction is one of the top ML use cases in the healthcare industry. The use of AI and ML-based applications in the healthcare service sector is increasing for predicting life-threatening diseases and improving patient care services.

The predictive modeling feature of ML algorithms derives patterns into the patients’ health reports efficiently and predicts the disease seniority. It helps physicians make immediate and better decisions to improve the patients’ outcomes.

2. Streamlines The Process

One of the major roles of ML technology in healthcare sector is process automation. Intelligent ML applications in healthcare sector, automatically processes data (patient data analysis), minimizes manual interaction, and maintain quality and accurate information. Hence, it will improve the operational efficiencies, optimize the resources productivity, and reduce the costs.

3. Research and Drug Development

It is the best use case of ML in the healthcare sector. Incredible predictive capabilities of Artificial Intelligence and Machine Learning are making drug discovery & development, candidate scope analysis, and patient data analysis for clinical trials faster and easier.

Further, ML technology is also being used for making faster decisions in drug design and development.

Moreover, besides research works, drug manufacturing companies are also increasingly investing in ML application development for forecasting the side effects of a drug candidate. It means ML apps in drug development are used for detecting the toxicity of a medicine before the clinical trial stage and improving its quality.

4. Efficient EHR Management

The need for ML in healthcare, especially for streamlining the cloud management and accessibility of patients’ data is going to drive more opportunities for ML healthcare applications in the future.

Features-rich ML-based applications in healthcare will help healthcare service providers and physicians access the Electronic health records of patients anytime from anywhere.

While ensuring the privacy of the patient’s health records, ML applications allow physicians to access previous medical treatments and the current status of their health condition. It was proved that AI and ML applications can help doctors predict the diseases that going to affect them in the next coming 5-10 years.

Hence, ML software solutions in healthcare play a key role in saving a lot of manual time in recording patient’s manual data and improving health outcomes.

5. Improve Diagnostic Accuracy

AI and ML-based medical image processing applications offer 99% accurate analysis on blood samples, DNA sequences, and radio images. Faster but accurate data analysis and patterns recognition will helps doctors to provide the best care and diagnosis services for reducing the health risks.

6. Treatment Suggestions

The use of machine learning applications or tools in healthcare makes the diagnosis process efficient and helps doctors to find multiple treatment or medicine suggestions to improve the patients’ health conditions. Based on the previous medication history and health conditions, AI and ML applications offer personalized treatment ways that ensure potential health outcomes.

7. Medical Device Performance Analysis

Intelligent ML applications make an impact in healthcare in many ways and medical device performance monitoring and analysis is one of them. AI/ML-enabled medical devices advance the accuracy of findings and improve therapeutic efficiency, thus patient care level will be boosted.

8. Virtual Nursing Assistants

ML-based virtual nursing assistants help hospital staff monitor the conditions of multiple patients at once. It is not possible to manage or view the vital health signs of many patients at once manually, but AI and ML-based software solutions do it efficiently. Hence, it will assist the staff to send immediate alerts to the physicians and make them aware of patients’ health conditions and improve care level.

9. Robotic Surgical Procedures

With unimaginable precision and adaptiveness, ML and AI-powered surgical robots have been making a buzz in the digital healthcare industry. Well-trained AI and ML-based surgical robots infused with the capabilities of professional surgeons are involved in minimally invasive surgeries. These AI and ML-based robot-assisted surgical robots will offer surgeons and ensure better visualization to perform surgeries with very small incisions.

These are a few significant applications of ML technology in the healthcare industry. Machine Learning like advanced analytical technology will benefit in terms of saving time, reducing operational costs, streamlining medical records management operations, and overall transforming the traditional healthcare operations from front-desk record keeping to complex surgeries.

How Much Does It Cost To Develop ML-based Healthcare App?

The cost of healthcare app development depends on various factors. There are so many types of healthcare apps available in the app stores. Virtual trackers, Diet planners, fitness & wellbeing apps, telemedicine apps, database management apps, medical networking apps, ePrescription apps, insurance claiming, and billing/invoice preparing apps, etc.

Based on the type the healthcare application, the features and design complexity will vary and this impacts the final cost of a healthcare mobile app.

Further, the application development platform, UX/UI design, technology stack used for mobile application development, and team size of the app developers will impact the final cost of the healthcare application. Moreover, the region and hourly rates of top app developers (Android app developers or iPhone app developers) will decide the actual cost of healthcare app development.

On a rough estimate, the cost of a healthcare application with a minimum level of design complexity and a set of the most required features will cost somewhere around $45,000 to $88,000. However, based on all the above factors, the cost of a healthcare application development might fall in the estimated range or exceed the limit as per your app specifications.

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Final Words!

The benefits of Artificial intelligence in healthcare or Machine Learning in the healthcare industry are numerous. AI and ML applications reshape the way healthcare service providers deliver services.

As we discussed in this article, AI-powered administrative and ML-based patient care solutions will be the future of the healthcare industry. AI and ML applications automate the front-office tasks and helps doctors improve care level.

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In the current scenario for ai in business application, data became crucial for every industry around the globe, and you know that 90% of the available information is generated in seconds leveraging intelligence Artificial Intelligence tools and it is impossible to gather such massive data from the human workforce.

AI based business intelligence applications are now a trend across the world. Organizations across various industries are implementing numerous applications of AI in business management and renovating their operational and functional flows to achieve their objectives in this digital space.

AI’s most significant technology Machine Learning (ML) is also disrupting the business intelligence mobile app development industry. To evaluate performance and match the progress with the targets, identify customer needs, predict customer preferences, and so on, organizations are increasingly investing in AI and ML-based business intelligence software applications and measuring their performance progress with ease.

Hence, the use of AI in business intelligence applications development is increasing since over the past few years. Today, in this article, we would like to walk you through:

• Practical examples of Artificial Intelligence in business intelligence applications that keep organizations competitive
• Applications Of AI In Business Management
• Reasons Behind Why Businesses Need AI-Powered BI Systems

Herein, we have discussed a few major business intelligence application development vendors like SAP, Siemens, GE, and other leading BI app developers that are designing and developing AI based business intelligence applications for businesses across various industries.

Practical Examples Of AI In Business Intelligence Applications

Here are the top use cases of AI-based business intelligence applications –

1. SAP’s HANA Platform

SAP is a Germany-based leading custom software development company. SAP platform consists of different models in their gallery, but as per our requirement, we are going to discuss SAP’s cloud platform HANA.

Most organizations use this software to manage databases of information they have gathered. To be more clear or precise, it will duplicate & ingests structured data like customer satisfaction from an app, relational databases, and many other sources.

You can install this HANA platform in different ways; one is by running on-premise via a company server or using a cloud source. The platform will collect information with the help of access points like financial transactions, equipment at production plants, desktop computers & mobile, and sensors across various business verticals.

If your salesperson is using a tablet or Smartphone to document purchase orders, and the data will be collected from those recorded transactions, which can be examined and analyzed by HANA platform to know customer or user problems and choices.

Walmart is one of the top retail chain stores (11,000) in the world and the USA, which doesn’t require any introduction. It is using the HANA platform to record & process its high-volume transactions that happen in 10 seconds.

2. Avanade’s Deep Analytics Platform

Avanade is the USA’s top IT consulting and services company that is engaged in providing artificial intelligence, business analytics, digital transformation, cloud migration, application development services, workplace management, and other enterprise-centric digital services and solutions.

Avanade is a company that is developed by two IT giants Accenture & Microsoft that has the capability to utilize Cortana intelligence and remaining solutions for data-based insights & predictive analytics.

Pacific Specialty, an insurance company that knocked on the doors of Avanade to develop a deep analytics platform with the focus to provide more information to its staff regarding the business. The insurance firm’s primary aim is to use policy & customer data to enhance team and company growth.

When you can understand your policyholders’ interests, trends, and behavior with the help of analytics, the company can give good advice about existing and new products that are available to the company.

Once, the company concluded that the coming future would be filled up with smart technologies where machines will do the maximum work that can be done by human resources. According to the study conducted by Avanade states that organizations can raise their revenue by 33% with the usage of smart technologies.

And they also revealed that it is going to create new job roles for professionals and many more benefits to users. It is also not precisely mentioned which professionals are going to be changed with the adoption of advanced smart technologies.

3. Apptus’ AI-powered eSales Platform

It is one of the best business applications for artificial intelligence. Apptus is a BI applications development company that is using AI for automation, streamlining operations, and enhancing the efficiency of processes.

Apptus has developed an AI-powered business intelligence platform- eSales for e-commerce and retail companies. This revolutionary application makes use of ML, NLP, predictive analytics, and deep learning like AI technologies and assists companies in automatically processing customers’ data and maximizing sales.

Apptus’ eSales platform identifies and analyzes data related to customers’ search and purchasing behavior and helps e-commerce service providers to display or send personalized product recommendations automatically. It will increase customer loyalty and optimizes sales value.

E-commerce companies are exploring 100% sales benefits using this platform. Based on search patterns, this AI & ML powered BI solution automates demand predictive tasks and creates conversions with ease.

Still, the technology is in the adoption stage, Cloudera Founder & CTO, said that deep learning is very good at anomaly detection and prediction. He also said it is getting simple for deep learning networks to comprehend what information is exactly authentic. And he also says, you cannot teach the platform what to work on, just provide a chunk of data from which it will sort out what it requires.

4. Siemens’ AI-based Reporting and Analytic Platform

 Siemens is using its ML technology to monitor and validate how its industry machinery equipment is working. The company launched MindSphere, an open industry cloud platform in beta.

The primary focus of this cloud platform is to monitor machine performance and detect defects for service requirements with the help of machine tools & drive train analytics.

This AI-powered BI application is being used by many industries to keep an eye on machinery and measure key performance metrics. Such prediction into devices will help companies make informed decisions about anticipatory maintenance & also be used to manage their equipment efficiently so that they can have a long lifespan.

When you compare Predix with MindSphere, the Siemens platform can work efficiently on every machine and plant regardless of the manufacturing industry. The core intention of the platform is to help plant operators to increase the uptime of their equipment and makes maintenance more competent by predicting when there is a possibility of machinery breakdown.

By using these types of platforms, industrial plants are seeing a reduction in maintenance costs. Siemens will provide a box whenever you opt for MindSphere, which you can attach to the machines, and it will collect the information related to the performance of the equipment by which the engineer can take action.

5. GE (General Electronics)

The latest technologies are taking a major part in the newest advancement in various industries. The usage of sensors increasing in physical equipment like vehicles, equipment spaces, machinery, and production plants, and these can be automated & analyzed by artificial intelligence.

When it comes to IoT, it is not about just consumer gadgets, oil rigs, commercial trucks, cargo ships, and trains can be automated or digitalized, examined, and predicted through networks.

Industries like aviation and oil & gas are using GE’s Predix operating system to know the historical performance data of the equipment by using the advantage of the industrial apps, which can be used to identify different types of operational outcomes like when there is a possibility of machinery failure.

If you think GE’s operating system is only for automating primary operations, then you have mistaken because it can process a large amount of information and prepare a forecast report within seconds.

The oil & Gas industry is using Accenture’s intelligent pipeline solution to examine pipelines that are a million miles across the globe. It gathers information from the pipelines & external sources for the safety and proper use of the resources.

When it comes to the airline industry, they are using an app called Aircraft Landing Gear that is built on Predix. The app helps airline engineering crews to check for how many days it will be in service before a flight is placed into the service. The app will prepare a schedule depending on the information that helps to minimize unexpected or unplanned equipment issues & flight delays.

For instance, this AI-based BI solution maximizes the performance of the equipment. After Pitney developed an automated solution on top of Predix, it raised its machinery yield by 20%.

These are a few examples of AI-enabled business intelligence applications. The above-listed BI applications developers are making use of revolutionary AI and ML technologies and tools and creating top-notch BI solutions for companies across all sizes.

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Now, let’s take a look at the best use cases of AI in Business applications.

Applications Of AI In Business Management

The integration of AI, ML, predictive analytics, and deep learning technologies into BI applications will help organizations identify and predict market trends, customer behavior, and optimize overall performance progress.

Custom AI app development companies are assisting enterprises in building futuristic AI based business intelligence applications and playing a major in digitizing data collection, processing, and analyzing operations.

Let’s move on to how can AI empowers Business Intelligence Applications or ai application in business or artificial intelligence in business management.

• Integration of Artificial Intelligence in business application optimizes features and functionalities of BI
• AI and ML in BI applications make data storing and processing faster and help organizations derive valuable insights into customers’ data
• AI development companies are adding intelligent tech capabilities to BI applications to define and intelligently process raw information
• Artificial intelligence in business intelligence, along with ML, and deep learning algorithms, smoothly segregates input data and simplifies complex data analysis processes
• A blend of AI and Natural Language Processing (NLP) makes BI software solutions better understand voice commands and perform data analysis tasks accurately as defined or required by the organization

These are a few benefits of AI Business Intelligence applications. Operational efficiency, productivity, analytics and insights, quick decision-making, business scalability, virtualization and visualization, and cost savings are all key advantages of using AI in business intelligence applications development.

Hire top AI applications development companies and get your BI solution developed with a sort of enterprise-friendly features and functionalities.

Reasons Behind Why Businesses Need AI-Powered BI Systems

The explosion of new big data sources, such as mobile, tablets, and the Internet of Things (IoT) devices will no longer undermine businesses.

They need increasingly practical experiences. This prompts AI-driven BI frameworks that will dramatically change business data into simple, precise, real-time narratives and reports.

BI Apps Delivers Data Insights– AI in business applications for deriving insights is gaining popularity. Big data growth in the market makes it difficult to make strategic decisions within the deadline. In recent years, Artificial Intelligence has increased BI systems to provide dashboards that provide alerts and business insights to key decision-makers.

AI in Business Applications Fills Resource Gaps – There is a shortage of experts with data analytical skills worldwide, and the well-developed country, USA also has a shortage of 1.5 million (approx.) data analysts. Therefore, it is very important to hire data experts in each department of a company to complete the given tasks.

Preventing Data Overload – Data is growing at an unimaginable rate these days and can easily choke off the business activities of organizations. This is where AI-powered BI tools come in, when a company has data bursting its BI platform from different sources.

It aids to analyze all the information and provides customized insights. Therefore, investing in AI-based BI software can help organizations break down data into maintainable insights

Final Words

We conclude that the need for Artificial intelligence in business application development is high. AI-enabled BI applications development will have a bright scope in the years ahead.

It is the right time for businesses across industries to invest in AI business intelligence software. Companies can reduce resource overheads, better manage enterprise data operations, build sales strategies, well-handle leads, and get 100% returns on investment using BI solutions.

USM is the best AI development company in the USA and India. Our seasoned AI-based BI applications developers create outstanding app development strategies and develop full-fledged automation solutions that scale up your operations.

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Machine Learning (ML) is a major branch of Artificial Intelligence (AI) technology. These technologies are revolutionizing the app development industry with their advanced analytics and automation features.

Over the past few years, incredible changes and advancements have been observed in mobile applications. The drastic transformation in application functionalities and performance continues to increase at a rapid pace. This is driven by the implementation of AI and ML-like disruptive technologies in software development.

The development of user-friendly and enterprise-centric ML and AI-powered software applications will enrich user experiences and ensure high-level satisfaction by integrating dominant tech features.

Yeah. Let me give you a detailed explanation of how machine learning technology will deliver smoother experiences to the users. Here is the best instance that might give you a clear clarification of ML’s advantage in mobile app development.

Most of us like online shopping. Am I right?

Yes. Amazon, Flipkart, and eBay are a few of the best online portals that use AI and ML capabilities to analyze customers’ search patterns and provide customized content recommendations to the users.

Similarly, microphone and in-app audio/video chat facility are all the innovations of ML and AI technologies. These features will help brands better engage their audience and improve sales conversions.

Similarly, the healthcare mobile app development market is too benefiting from using machine learning in their mobile apps. ML-based software is assisting healthcare service providers in delivering more personalized and instant care services to patients who are unable to visit clinics or in remote locations.

Likewise, ML algorithms are widely applied in fitness trackers development, finance apps development, and data-driven apps development.

Today, in this article, we would like to give you brief information on how ML technology is influencing the app development industry and how ML can be gainful for mobile application development.

Here we go! 

How ML Technology Is Impacting App Development Industry?

As we discussed above, the role of Machine Learning (ML) in app development is appreciable. ML systems or applications will take customer-related or business-centric data as input and deliver informed recommendations that drive the company’s growth.

Why ML Is Using In Mobile App Development?

 Compared to the other AI technologies, machine learning technology will make apps more robust, accurate, and powerful. They are more productive and accurate in generating final results than compared to any other mobile technologies.

Mobile app developers focus to develop user-friendly apps that ensure user personalization and their experiences. Hence, they are integrating ML algorithms to provide highly accurate results.

Let’s take a look at the way companies are using ML in mobile apps.

• For Predicting Future Trends 

It is one of the most popular reasons for using ML in mobile app development. ML software help companies retrieve results-driven insights into the future. We mean to say that ML applications are enough capable to analyze current market trends and evaluate the upcoming business opportunities.

So, when it is integrated into mobile apps, the business can understand the user requirements and send personalized content recommendations. Hence, a company can promise greater convenience and satisfaction through ML applications.

• Saves Time In Searching For Content

ML tool integrations in mobile apps would help your users find exact information in seconds. ML algorithms will interpret queries, and analyze data records with a few keywords, and the results are really promising.

• Streamline & Secure App Authentication 

Machine learning in mobile applications is best useful for ensuring high security and user authentication. Leveraging the power of facial, fingerprint, or voice recognition technologies, ML apps will authenticate users in multiple steps and streamlines app accessibility.

• Tracks & Prevents Nasty Traffic   

ML tools and algorithms are more capable to detect fake traffic or fraud data which is trying to reach your mobile device. So, ML apps will play a major role in preventing suspicious activities. 

These are three major reasons for integrating ML technology in mobile software development. Mobile app development companies are focusing to make apps as user-friendly as possible.

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What Are The Advantages Of Using ML In Mobile Apps?

Here are a few benefits of using Artificial Intelligence and Machine Learning in mobile application development.

• Machine learning application development will help in predicting market dynamics
• Helps in processing vast databases and deriving insights for taking proper business actions
• Machine learning for mobile apps is beneficial to deal with security concerns
• The concept of machine learning, along with AI and NLP assures next-level app performance by interpreting user speech and delivering automatic responses to them instantly and accurately.
• Machine learning applications in manufacturing will detect anomalies in the devices, prevents downtime, and improves productivity.
• Machine learning app development for retail or e-commerce will forecast product demand and let companies maintain inventories.
• Likewise, advanced search facility, customer-support Chatbot, fraud detection, forecasting trends, and sending customized product recommendations are all a few top benefits of integrating machine learning into mobile apps.

Now, let’s have a look at the best mobile apps that used the power of ML technology and got success in the industry.

List Of Top Machine Learning Applications

 Here is a list of the most popular applications that are developed using ML capabilities.

1. Tinder

Tinder is one of the leading dating apps which is available for Android and iPhone users. This application has reported over a hundred million downloads as of March 2022.

Using machine learning algorithms, Tinder has developed the Smart Photos feature. Due to this ML-based feature, the Tinder application tracks user search patterns and swipes to find the perfect matches. This ML feature in the Tinder app will save the user time in searching for their soulmates.

2. Apple Siri

It is another best example of Machine Learning innovation. It uses AI logic and ML algorithms to quickly understand the user commands and respond to them immediately with 99.9% results.

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3. Snapchat

Snapchat is the best app for online communication. Users can send unlimited photos, videos, and moments instantly. It is developed with a combination of ML and Augmented reality capabilities. Users can experience trilling augmented reality experiences.

The app uses ML-based facial-tracking algorithms and devices’ front to draw facial features and create a Bitmoji for a fun-oriented chat.

4. Netflix

The ML-based recommendation system is the major source for Netflix for increased subscriptions and profitable business. Based on the user’s search input queries and watch history, Netflix applications offer extremely personalized video recommendations to the users.

Hence, ML in Netflix app development is helping the brand to monitor user search and behavioral data and send the desired content based on their preferences.

5. Google Maps

Did you ever think of how google is directing us and how it is finding parking slots online? This is all the power and magic of ML algorithms. With the combination of AI and ML tools and techniques, the application is capable to analyze geodata (which are collected from the user devices) and navigating the users towards their destinations in the shortest paths.

These are a few mobile apps that have used ML in their development process to deliver highly personalized app experiences.

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Wrapping Up

Machine Learning, Artificial Intelligence, and Speech Recognition technologies will change the ways people interact with their devices, apps, and brands. They will make applications more creative and interactive to promise personalized experiences.

Though integration and implementation of machine learning technologies are still at the toddler stage, rapid developments in AI and increasing demand for digital solutions would expect to draw many investments into ML development in the upcoming years. From faster decision-making and demand prediction to demand prediction, ML software is highly beneficial.

If you are in plans to hire ML development companies, USM is the best app development partner for you. We integrate ML capabilities into the native mobile app development process and develop state-of-the-art mobile applications within deadlines.

Being an ML development company in USA, our team of app developers make use of the right technology stack and promise you an out-of-the-box mobile application.

Our #mobile app developers in Texas, USA will integrate AI and ML features in route navigation apps, voice search applications, Language translation apps, location-tracking apps, and photo/video sharing apps.

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#Machine Learning Development Services, #AI/ML Software Development Company, #Machine Learning Development Companies In Virginia, USA, #ML Services and Solutions Providers USA, #Machine Learning Development Company, #Mobile Application Development Services USA

Is Machine Learning Adapt The Manufacturing Industry?

In our BLOG STOP page, we already explained the following topics,

• The benefits and risks of artificial intelligence
• Artificial Intelligence Market Predictions
• How AI revolutionizes businesses?
• What Can Artificial Intelligence Do For Retail Industry?
• The Significance Of AI Technology By Industry
• How Many Of You Agree That “AI is impacting The “Mobile App Development Industry?
• What kind of benefits delivered by AL & ML to the banking and finance sectors
• 5 Examples That Show How Machine Learning is Changing Modern Advertising Industry

All of the above information blogs focus on helping the reader learn how to achieve exact business results using Artificial Intelligence and other emerging technologies.

Now, we will discuss how Machine Learning (ML) in the manufacturing industry automates processes. This article may give a few insights into developing technologies for manufacturers. Let’s move on to the topic.

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The role of Machine Learning in Manufacturing

Though the concept of Artificial Intelligence has been in the market for decades, now, it’s gaining momentum. It is a valuable asset to many industries. Using AI, the companies are observing smart business processes.

It does not matter how they use AI for manufacturing, but AI and ML help businesses create smart business strategies. Yes. Smart technologies are like gears for modern manufacturing industries. Moreover, AI & ML are playing a vital role in the manufacturing industry. They help manufacturers in finding new business ways that optimize existing operations.

The manufacturing industry always needs new ways in the design and development of equipment’s/products. AI and Groundbreaking benefits of Machine Learning for Manufacturing are the best technologies for the manufacturing industry.

Implementation of Machine learning in the manufacturing industry will improve productivity without compromising on product quality. However, with changing customer preferences and need, the proliferation of new product demand is growing in manufacturing today.

To overcome this challenge, most of the manufacturers are shifting to ML technology. They have confidence in ML as it can enhance the performance of end-to-end manufacturing operations. ML in manufacturing provides a solution to prevent losses in product development.

Now, we will move further into major applications of Machine Learning in the manufacturing industry, along with real-world examples.

Applications of Groundbreaking benefits of Machine Learning for Manufacturing

#1 Machine Learning for Predictive Monitoring

ML models help manufacturers in monitoring equipment failures. ML-based predictive maintenance solution allows manufacturers to predict device failures accurately. The predictions help manufacturers reduce planned equipment maintenance. This approach of predicting device conditions offers enhanced product reliability, quality, and durability.

Further, the ML-based monitoring and prediction systems can schedule device maintenance for particular time intervals. Hence, ML is engaged in performing repetitive tasks without human involvement. Therefore, ML in manufacturing offers business scalability and cost structure.

The best example of Machine learning in manufacturing for equipment monitoring:

A leading automotive plant has implemented a predictive maintenance ML solution for their hydraulic press. This ML solution predicts device failure with 92% accuracy, plan maintenance, and ensure product reliability. Total equipment efficiency has risen to 85% from 65%.

#2 ML Technology For Quality Control

ML plays a significant role in analyzing the performance and quality of assembly products. ML in manufacturing finds product weakness faster and helps to reduce the performance errors in machines.

Thus, ML-driven computer vision algorithms can automatically learn from product data and detect product defects. ML can also perform automatic quality control tests that can detect device faults with over 90% accuracy.

#3 ML For Inventory & Logistics Management

Manufacturing industries are not only focused on assembly line and production functions, but they also give equal importance to their supply chains and logistics operations.

In traditional methods, order data collection, order value calculation, logistics performing, and product-related tasks done manually. But, deploying ML in manufacturing can efficiently handle issues in logistics services and cuts unnecessary costs. 

Moreover, with a successful blend of artificial intelligence (AI), ML, and IoT with asset tracking sensors, the emerging technologies improve and automates supply-chain management operations. Besides, by monitoring every step of manufacturing processes and production, machine learning in manufacturing also optimizes inventory management.

Best ML Solution for Supply Chain Management

Another strength of machine learning-based algorithms is in resource management. To know how powerful it is, have a look at the power-consumption optimization algorithm that Google has reduced to 40% on its electricity bills in its data center cooling systems.

#4 ML for demand forecasting

It is one of the best mobile applications cum benefits of machine learning in manufacturing. AI & ML algorithms can integrate with procurement and cost management fields. It will improve the accuracy of product demand prediction.

Using historical product data, ML models can provide insights into market dynamics and make quick decisions for gaining sales profits.

How ML applied for demand forecasting?

The role of machine learning in predicting product demand is vital. ML in manufacturing analyzes historical sales and product demand trends. Later, for improving forecast accuracy, the ML tools also dig deep into historic product discounts and price fluctuations, and all.

Like this, ML helps manufacturers in predicting product demand. Such an AI strategy benefits manufacturers grab sales opportunities by maintaining adequate inventory and giving the best price to the product.

#5 Significance of Robots in Manufacturing

It’s a fact that robots play a prominent role in the manufacturing industry 4.0. The use of industrial robots in performing repetitive manufacturing tasks is increasing rapidly. The robotic-powered manufacturing process offers ample opportunities to the manufacturers in achieving agile production and reduces human-errors.

#6 Automated Guided Vehicles (AGVs)

Manufacturing industries are using AGVs in the production and assembly environment. These AI and ML-powered autonomous vehicles can carry large components with ease. The best this about AGVs is they can adjust their route by detecting objects or sensing humans.

#7 ML for Generative Design

Generative design software use ML to mimic the actual design. Using input parameters- materials, size, manufacturing methods, product weight, the generative design software delivers maximum possible design outcomes using those parameters.

Thus, machine learning in manufacturing helps manufacturers create hundreds of designs for a single product in minutes.

Few of other benefits of ML in Manufacturing-

Along with the above applications of ML in manufacturing, the industrial ML solution for manufacturing also offers the below benefits to manufacturers.

• Reduce poor-quality products and increase throughput
• Reduces cost overheads through predictive maintenance
• ML offers synchronized production flow
• ML ensures robot-human collaboration in the workplace
• Boosts manufacturing processes
• Gives insights to manufacturers for designing consumer-focused products.

Final Thoughts

Machine learning in manufacturing offers a plethora of benefits to manufacturers. From equipment maintenance and production to inventory management and supply-chain management, ML plays a vital role in the manufacturing industry.

ML in manufacturing can identify, monitor, and analyze device conditions during the manufacturing process. Using machine learning, the operators can alert before the product or device gets damaged.

USM, a leading artificial intelligence and machine learning service provider, helps your business grab benefits from emerging technologies. Our expertise in using AI and ML technologies helps various industries in fueling their business.

USM Business Systems AI-driven solutions empower manufacturers to bring automation in manufacturing tools, machines, procedures, and analytics. We help you to scale the manufacturing automation.

Our expertise in delivering custom-build Artificial Intelligent mobile apps for manufacturing made us a leading AI service provider in the market.

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Machine learning (ML) is a revolutionary technology that helps industries optimize their day-to-day processes. ML technology has made its mark in supply chain optimization and maintenance.

Supply chain optimization or maintenance requires the business to examine the data in real time and explore sales opportunities constantly. Whether it is a semi-automated or manual process, Machine learning technology now does not require manual intervention in the supply chain.

ML algorithms regularly analyze the data of the supply chain to derive new patterns. It helps businesses determine new opportunities to optimize their supply chain management processes. ML algorithms process data using limit-based modeling to find the set of factors affecting the supply chain with data presence-accuracy.

According to the research reports, 79% of companies with well-optimized and high-performance supply chains achieve higher revenue growth. Machine Learning in the supply chain offers optimized supply chain operations and saves operational costs.

In this blog, we have compiled the best information that lets you be aware of why is machine learning important in the supply chain and how the use of machine learning in supply chain management ensures business benefits.

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Why Is Machine Learning Important In Supply Chain?

With a focus to optimize the efficiency of supply chain and logistic operations, manufacturing, retail, real estate, healthcare, e-commerce, and many more industries are switching to ML applications.

Let’s look at how machine learning will address the best solutions for solving complex challenges in logistics and supply chain industry.

• ML applications assist companies in maintaining adequate quantities of products as per the demand
• Faster and reliable deliveries
• Delivers insights into sales data and helps companies in exploring business opportunities
• The role of Machine Learning in supply chain in reducing costs and improving business efficiency is incredible.

Top ML Use Cases For Supply Chain Management

The use of Artificial Intelligence and ML can streamline entire supply chain operations. A few of the top use cases of machine learning in supply include production planning, inventory levels management, quality checks, orders management, demand forecasting, payment collection, logistics visibility, etc. Like these, the adoption of ML will be the best solution for many challenges in logistics and supply chain industry.

Hence, the role of machine learning in supply chain and logistics will not permit these applications. Monitoring fleet movements is also the best application of machine learning in supply chain industry.

Know more about ML Use Cases for Supply Chain Management and transform your operations competitively!

Advantages Of Using Machine Learning In Supply Chain

Here are a few top benefits of machine learning in supply chains.

• Accurate demand forecasting using the predictive analytics feature of ML technology
• Using AI, ML solutions are used for automated quality inspections to find product defects
• Great visibility across the supply chain and distribution
• Reduces the complexity in production planning and optimizes demand forecasting operations
• Minimizes delivery times and improves customer experiences
• Mitigates the risk of overstocking and ensures better warehouse management
• Using IoT power, Ml apps would help in tracking the fleet 24*7

These are a few significant advantages of using machine learning in supply chain operations.

Here are the best examples of how companies are benefiting from ML solutions.

XPO Logistics Deploys Machine Learning To Optimize Supply Chain:

As the corona virus epidemic continues to arouse interest in e-commerce purchases, XPO Logistics (NYSE: XPO) is promoting technological solutions to meet the demands of carrier speed and agility, along with reverse logistics and inventory management.

The honorable chief information officer of the transportation and logistics giant, Mr. Mario Harrick had recently discussed the company’s technical strategy during a fireside chat with the president of Freight Waves named ‘George Abernathy’. This tech conversation took place at the American Shippers Global Trade Tech Summit.

Harrick said that “The way you implement on a rapid supply chain, on a more efficient and effective supply chain, is by using our technology”. He also said, XPO Company invested 500 million dollars on years on its proprietary technology systems, focusing on four categories.

The company uses AI and ML technologies to analyze consumer demands and estimate inventory for all its retail customers. So there’s a better way for drivers, offering end-to-end shipment visibility and sequencing optimization in other applications, he said.

XPO Connect, the digital freight market of the company has over 60,000 carriers, and the organization is using machine learning technology to become smarter in helping carriers, and shippers sell and buy capacity. The latest COVID-19 dashboard enables customers to visualize the impact of the epidemic on the supply chain.

Another field of interest is warehouse optimization. As shippers switch from moving pallets for retailers to products purchased individually for consumers, they require more efficient warehouses. Autonomous robots are one of the best solutions that make item picking safer and more efficient.

Also Read: Uses Cases of AI in Supply Chain Management

Hence, the use of machine learning in supply chain management is also increasing across leading brands like Microsoft, Alphabet, and automotive car manufacturers to improve the efficiency of their supply chain and logistic operations.

Final Thoughts

Machine learning is a booming technology that laid the foundation for the next-generation logistics and supply chain ecosystem.

As we discussed in this article, the benefits of machine learning in supply chains and transportation visibility are incredible. ML offers insights into improving supply chain management performance through flawless delivery management, improved customer experience, enhanced inventory planning, and optimized cost.

Ready to deploy Machine Learning to optimize your supply chain, contact us today.

USM Business Systems is a leading technology partner serving a wide range of industries with Machine Learning solutions. Get a free quote for your project!

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Machine Learning (ML) is a significant application of Artificial Intelligence. As we discussed in our ML blog category, applications of machine learning in healthcare learn from previous experiences or data, identifies patterns, and forecast future events.

These predictions add value to businesses. This was the major reason behind the increased demand for ML techniques. Several industries are using ML in their multiple applications for various purposes.

It’s a globally known fact that the digital world is making data accessible in a simple way. The vast amount of data is collecting, storing, and processing easily using intelligent technologies. On top of this, increasing the computing efficiency of innovative technologies made data profitable for businesses.

In addition, processing toolkits, open-source frameworks, and immense libraries are all available at affordable prices, and thus, building and executing ML applications is growing worldwide.

Though ML is giving lucrative benefits to industries, its impact in the healthcare industry is unbelievable. Machine learning in healthcare leads to some thrilling developments in oncology. Diagnosis of cancer and providing accurate treatment at the right time will become more feasible in the coming years with ML.

Machine learning algorithms the sensitivity-level of diagnosis, and helps physicians to make the right decisions to provide value-added treatments. Therefore, ML-based machines assist healthcare service providers to offer personalized treatment experience to their patients.

Yes, this reinforcement learning can also improve employee productivity in hospitals. Of course, the applications and uses of ML in the healthcare sector are infinite. In this blog, we briefed a few major uses cases of Machine learning algorithms in healthcare. Let’s start!

#1 Disease Detection

Healthcare machines are overloaded with increasing usage. This was primarily due to a growing population and with the same proportion of increasing diseases. The machines are continuously working to identify the diseases of the patients.

However, traditional machines are not capable of doing this efficiently. But, machine learning models can detect diseases with more accuracy at less time. It’s a proven fact!

Researchers have been still working on exploring machine learning in the healthcare sector. They are continuing their innovation in developing more efficient models of machine learning. Currently, machine learning methods help doctors in the early detection of diseases before the disease reaches to severe level.

The global leading technology companies are developing neural network artificial intelligence systems for accurate detection of difficult respiratory disorders. These machines, in connection with the IoT-based sensors, can precisely analyze respiratory problems and avoid risks and hospitalizations.

Nowadays, new deep learning-based disease prediction methods efficiently identify cancer in early stages. These models are trained with the patient’s medical records to detect irregular patterns that cannot be observed by human physicians.

#2 Robotic Surgery

Today’s intelligent robots are completely altering the way of doing surgery. They are designed to perform complex surgical functions. Robots can also reduce the time of surgery.

Robots can perform cardiac, spine, orthopedic hip and knee replacement, neurology, cancerous tumor irradiation, and other significant surgeries. Robotic surgery is minimum invasive and offers 99.9% positive patient outcomes.

Explore more about USM’s AI & Machine learning apps in healthcare

AI and ML-based surgical tools can enhance the processing of medical records. Also, they can give self-reviews based on past surgical experience. Therefore, the use of robots optimizes the process of surgery and reduced errors that are may happen with physicians.

#3 Personalized Medicines

The current technology is restricted doctors to estimate the risk level of disease based on the patient’s previous medical history and genes. Using AI & ML, the researchers are finding new ways of developing further matured personalized medicine for various diseases.

Machine learning in medicine is shaking today’s healthcare sector. ML-machines can provide numerous treatment ways when they trained with the patients’ medical records. In the next coming years, we will surely observe the manifold of intelligent devices and sensors with infinite capabilities.

#4 Smart Health Records

The manual process of keeping healthcare records up-to-date is a bit complex procedure. Though technology is playing a significant part in enabling data set entry procedures, still, the process was very lazy and taking too much time to enter the training data.

Here, unsupervised learning comes into place. The major role of machine learning in healthcare is to make every process simple, complete the task in a very short span.

So, ML-based smart health records avoid manual errors and automatically update the patient’s health data. Thus, the updated learning from data is very much useful for doctors to provide treatment/medical suggestions to the patients.

#5 Research and Clinical Trials

Machine learning has been widely using in medical research and clinical trials. Clinical trials of new drugs are cost-efficient, and take years to complete and get approvals from authorities.

Many pharmacy companies are now using ML-based predictive analytics for identifying latent clinical trial candidates. Machine learning models prefer electronic records to avoid unlabeled data-related errors.

#6 Improved Radiotherapy

One of the major use cases of machine learning in healthcare is in the radiology field. ML-driven algorithms can automatically learn from massive various samples to detect the patterns.

Using machine learning, the radiologist can easily categorize regular or irregular impressions in a medical image. This scenario helps to diagnose the disease level and provide proper treatment at the right time.

#7 Disease Predictions

The power of AI and ML technologies in healthcare is incredible. The government and private healthcare service providers are deploying AI & ML-based systems for many purposes.

Among other, ML and predictive analytics benefit healthcare providers to accurately predict a wide range of diseases from malaria to chronic and infectious diseases. This capability of ML helps for those markets that have no proper medical infrastructure for identifying such kind of diseases.

#8 Drug Discovery and Manufacturing

Drug discovery was a major clinical use case of machine learning. Next-generation sequencing and precision medicine technologies are also widely using in detecting multiple ways to provide therapies for various diseases.
The use of ML in drug discovery will help to reduce the cost of introducing new drugs to the market, and also make the drug discovery process faster.

#9 Crowdsourced Data Collection

Nowadays, crowd sourcing in the medical industries is all the rage, enabling practitioners and researchers to obtain high amounts of the data uploaded by individuals depending on their own consent. There are tremendous changes in the way medicine is perceived in these live health data.

Tech giant, IBM has recently associated with Medtronic to decipher, accumulate, and make available insulin and diabetes data depending on the crowd sourced information.

As progress is made in the Internet of Things (IoT), the healthcare sector is finding new ways to use this data and to address tough-diagnosis cases and the comprehensive development of diagnostics and drugs.

#10 Machine Learning-based Behavioral Modification

Behavioral modification is a significant part of preventive medication, and since machine learning usage has expanded in the healthcare industry, a large number of startups have been growing in areas such as cancer recognition and prevention, patient diagnosis and treatment, and more.

Somatics is a B2B2C based data analytics firm that has released an ML-based applications to identify gestures we make in our day to day lives, which allow us to understand our unconscious behavior and make necessary changes.

Wrapping Up

USM Business Systems has vast experience and best practices in using AI and its technologies, deliver high-level AI and ML solutions and mobile apps. We have achieved excellence in AI and ML, but further developments in intelligent technologies are needed in this society.

Our machine learning services for healthcare have helped many clients to save their time and costs. Our Machine learning solutions enabled our customers in uplifting their offerings for this modern world.

Gain the benefits of Machine Learning in Healthcare by partnering with USM

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Click on the link to learn much more about Artificial Intelligence technology

A large number of online businesses believing that AI technology has a positive impact on their bottom line and offering a competitive advantage. Every crucial step, such as retailer’s website visiting, products adding to the cart, ordering, paying, and checkout can be automated using AI capabilities.

As per the research reports, AI will start using a minimum of 60% of e-commerce representatives by 2020. On the other side, 30% of revenue growth will be associated with AI technologies only.

Moreover, as per the PwC study, Artificial Intelligence usage will increase global GDP by 14%, i.e. approximately 15.7 billion dollars by the year 2030.

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Recommended to read: How Much Does Artificial Intelligence Cost?

Have a look at the below statistics report of AI technology in the E-commerce sector.

Today, in this blog, I’d like to let you know about the success stories of famous eCommerce companies that are implemented AI technology to leverage their business.

Now, let’s look into the

5 Success Stories of Artificial intelligence in eCommerce field

#1 Burberry

Luxury brands are reluctant to implement Artificial intelligence and switch to online shopping. The use of AI raises fears from the belief that wealthy clients will expect tailor-made items and one-to-one customer services. As everyone, brick and mortar shops are the most frequently purchased places in the luxury industry.

In 2006, suddenly, Burberry Company had decided to disrupt the industry and become an early adopter of the advanced technology named ‘Artificial intelligence’. Bridging the gap between offline and online customer experiences is the main aim of this firm.

Burberry has proven that the AI ​​business can take advantage of the competition and gain more customers. By 2025, 20% of luxury sales are expected to take place online, and this strategy has proven to be successful.

Thankfully, Burberry has proven that Artificial intelligence and machine learning can take advantage of the competition and win more customers. By 2025, 20% of luxury sales are expected to take place online, and this strategy has proven to be successful.

McKinsey stated that “As 80% (Approx.) of luxury sales are influenced by online eCommerce stores, the importance of online channels will not be ignored”. The share of tech-savvy purchasers like Genz and Millennials will be 45% of the global luxury market by the year 2025.

To benefit from Artificial intelligence, Burberry used RFID tags in its mortar and brick stores to collect the in-depth data about how customers behave in their stores. This data consists of past orders and online customer activity, and sales assistants can use it to meet consumers at brick and mortar shops.

In addition to AI implementation, the brand has also used big data to identify fake products. It is possible by the use of AI-based image recognition. This trending technology from Entropy assisted in identifying counterfeit products from the picture.

#2 StitchFix

StitchFix has transformed to online shopping in 2011. Collecting data on their style preferences from clients, StitchFix combines the expertise and AI of stylists. The end result – customized clothing unique to the client style.

Stitchfix recognizes style as a combination of features such as arm length, neckline, and color. After evaluating the features, the algorithm reassembles and then sends the final design to the stylists who create a list of them.

Apart from the design component, StitchFix uses AI to streamline its logistics and operations. After requesting for clothing, the algorithm selects the most convenient location of a warehouse, which is closest to the consumer’s location and finds the correct way to see all the goods being picked up at the same time.

#3 Starbucks

Will AI replace Baristas? Starbucks has proven this to be the case to some extent. In 2017, the coffee chain introduced the Virtual Assistant, which enables customers to order coffee and pay through online using voice or text. The human need to prepare a virtual assistant order is not excluded.

When a customer enters a Starbucks coffee store, the barista can retrieve information and place an order through a point-of-sale device in the client’s previous purchase history.

Unlike the text option, Starbucks has also unleashed Starbucks reorder skills for Amazon Alexa. Clients can submit their repeat order to the selected coffee shop by saying “Alexa, order my Starbucks”.

Starbucks also collects data on customers past order history. Based on that, models and their current preferences can be established between clients of previously ordered products.

Capturing meaningful insights into customer behavior, Starbucks was able to create appropriate recommendations for its clients. Extracting meaningful insights on consumer behavior, Starbucks can make tailored recommendations for its customers.

#4 Domino’s Pizza

The online distribution channel for Domino’s Pizza accounts for 55-58% of all orders. Domino’s uses the information to acquire a 360-degree overview of its trusted consumers.

For example, with the data collected, Domino’s can derive insights on who is the dominant buyer in the household, what channels they are using and how they are interacting with those channels. This helps make killer offers (just precisely the right pizza today), but also grant unique coupons.

With that information, Dominos can gain insight into who buys the most in the home, what online websites they use and how they deal with those channels. It helps to make exclusive coupons and special offers deals. In addition to using AI to understand customer needs better, Domino’s Pizza uses algorithms to tailor pizza quality and taste to its clients.

The pilot project revealed with a smart scanner that checks pizzas when baked in countries like New Zealand and Australia. Recommendations are made on “perfect” pizza, based on data previously entered into the system.

The AI-based scanner gives grade/score to each pizza from the Microwave oven to specific criteria based on the type of pizza, size of the pie, and the number of toppings. It is possible by feeding a picture of freshly baked pizza into the AI system and comparing with previously baked pizzas before delivering to the customers.

#5 Amazon

Last but not least, the leading online retailer store, Amazon has also adopted AI subsets like natural language processing (NLP), deep learning and machine learning (ML).

Amazon uses a pre-shipping model that uses data analytics to estimate which products have the highest purchase potential on the website and when the customer will need them.

Keep in mind that Amazon has a massive directory of things and the customer can simply get lost in multiple item categories, helping to reduce product visually search time, quicken the purchase and provide better shopping experience.

With accurate estimates of total product demand, Amazon can simplify its logistics, transferring enough products to warehouses to meet market needs. This means that delivery time reduced and demanded products remain in stock. Moreover, Predictive Analytics has been supporting to boost up the product sales and profits of Amazon.

Instead of estimating customer needs, inventory management and optimizing logistics, Amazon uses Artificial Intelligence to define the right price for each product. The cost is adjusted based on availability, competitors’ price, on-site activity, order history, expected profit, etc. As the customer data is analyzed and subject to updates, prices will be modified every a few minutes.

Final Words:

AI technology is widely used by famous eCommerce companies to optimize cost in real-time, create the best product design, predict the inventory and sales, to minimize fake products, and finally to create a great online shopping experience.

Case studies of Amazon, Domino’s Pizza, Starbucks, Stitch Fix, and Burberry have proved that they improved the bottom line and won over customers.

If you want to adopt AI technology and improve your e-commerce business growth and revenue, please feel free to contact us.

USM Business Systems has two decades of professional experience in providing AI and ML services. We have associated with 1000+ clients and helped them in growing their businesses.

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All of the above information blogs focus on helping the reader learn how to achieve exact business results using Artificial Intelligence.

Now, we will discuss on how AI & ML modernize the manufacturing industries. This article may give a few insights into developing technologies to manufacturers. Let’s move on to the topic.

Though the concept of Artificial Intelligence has been in the market for decades, now, it’s gaining momentum. It is a valuable asset to many industries. Using AI, the companies are observing smart business processes.

It does not matter how they use AI for manufacturing, but, intelligent technologies always help businesses to create smart business strategies. Isn’t it? Of course, it’s true!

Smart technologies are like gears for modern manufacturing industries. Moreover, AI & ML are playing a vital role in the manufacturing industry. They help manufacturers in finding new business ways that optimize existing operations.

The manufacturing industry always needs new ways in the design and development of equipment’s/products. It will enable manufacturers to excel in their productivity without compromising on product quality. However, with changing customer preference and need, the proliferation of new product demand is growing in manufacturing today.

To overcome this challenge, most of the manufacturers are shifting to ML technology. They have confidence in ML as it can enhance the performance of end-to-end manufacturing operations. It also provides a solution that helps to eliminate losses in product development.

Now, we will move further into major applications of Machine Learning in the manufacturing industry, along with the real-world examples.

Applications of Machine Learning in the Manufacturing Industry

#1 Machine Learning Enables Predictive Monitoring

ML models help manufacturers in monitoring equipment failures. ML-based predictive maintenance solution allows manufacturers to predict the device failures accurately. Also, instead of assigning new resources for planned equipment maintenance, using predictions, manufacturers can plan maintenance efficiently. This approach of predicting device conditions offers enhanced product reliability, quality, and durability.

Further, the ML-based monitoring and prediction systems can schedule device maintenance for particular time intervals. Hence, ML is engaged in performing repetitive tasks without human involvement. Therefore, ML apps in manufacturing offer business scalability and cost structure.

Example:
A popular automotive plant has implemented a predictive maintenance ML solution for their hydraulic press used in the production of vehicle panel. As per the study, engineers spend more time attending to disassembly without allotting resources for planned maintenance.

This ML solution allows predicting device failure with 92% accuracy, to plan maintenance more efficiently and to provide greater product quality and asset reliability. Total equipment efficiency has risen to 85% from 65%.

#2 ML Technology For Quality Control

Artificial intelligence and ML technologies are using to perform quality control and product inspection tasks. ML-driven computer vision algorithms can automatically learn from product data and detect the defects in the product. Besides, ML can also perform automatic quality control tests that can detect device faults with over 90% accuracy.

The agricultural sector is also gaining incredible benefits using intelligent sorting techniques. Using these systems, the operators can grade a product based on size and quality. This kind of ML solution helps to reduce the difficulty of the sorting procedure in agriculture.

Example:
An agricultural product packing equipment manufacturer have created a smart fruit sorting machine that uses machine learning and computer vision to classify the defects of skin.

The operator sorting platform can differentiate between different types of errors and sort the fruits into advanced pack grades. The solution combines software, hardware, and functional optimization to reduce the complexity of the sorting process.

#3 ML For Inventory & Logistics Management

Manufacturing industries are not only focused on assembly line and production functions, but they also give equal importance to their supply chains and logistics operations. An amazing quantity of logistics to aid the increased smart production process is essential for enterprises.

In traditional methods, companies perform manual tasks like order data collection, orders value calculation, logistics performing and product-related tasks. But, ML-powered manufacturing solutions can efficiently handle issues in logistics services and cuts unnecessary costs.

Example:
Another strength of machine learning-based algorithms is resource management. To know how powerful it is, please have a look at the power-consumption optimization algorithm that Google has reduced to 40% on its electricity bills in its data center cooling systems.

This was done without any alterations or infrastructure modernization – the big data passing through the system was adequate – making this feat even more impressive.

#4 ML for demand forecasting

AI & ML algorithms can integrate with procurement and cost management fields. It will improve the accuracy of product demand prediction.

Using historical product demand and supply data, ML models can provide insights into its market and allow manufacturers to take the necessary steps in product range and supply.

#5 Significance of Robots in Manufacturing

It’s a fact that robots play a prominent role in the manufacturing industry 4.0. The use of industrial robots in performing repetitive manufacturing tasks is increasing rapidly. Robotic-powered manufacturing process offers ample opportunities to the manufacturing company in achieving agile production and reduces human-errors.

Some Closing Thoughts:
The machine learning algorithm is an essential technology that provides advanced predictive maintenance. They can identify, monitor, and analyze device conditions during the manufacturing process. Using machine learning, the operators can alert before the product or device failure. As we discussed in this article, ML helps the manufacturer in many ways.

USM, a leading artificial intelligence and machine learning service provider, helps your business to get benefit from these intelligent technologies. Our expertise in using AI and ML technologies helps various industries in fueling their business.

USM Business Systems’s AI-driven solutions empower manufacturers to bring automation in manufacturing tools, machines, procedures, and analytics. We help you to scale the manufacturing automation.

Our expertise in delivering custom-build Artificial Intelligent mobile apps for manufacturing customers made us a leading AI service provider in the market.